Who Blinks First
55
THAD KOUSSER
University of California, San Diego
JUSTIN H. PHILLIPS
Columbia University
Who Blinks First?
Legislative Patience and
Bargaining with Governors
When legislators and governors clash over the size of American state government, what strategic factors determine who wins? Efforts to address this question
have traditionally relied upon setter models borrowed from the congressional literature
and have predicted legislative dominance. We offer an alternative simplification of
state budget negotiations that follows the “staring match” logic captured by dividethe-dollar games. Our model predicts that governors will often be powerful but that
professional legislatures can stand up to the executives when long legislative sessions
give them the patience to endure a protracted battle over the size of the budget. In
this article, we present our analysis of an original dataset comprising gubernatorial
budget proposals and legislative enactments in the states from 1989 through 2004.
The results indicate strong empirical support for our predictions.
Who is more influential—legislators or governors—when they
bargain over the size of American state budgets? What institutional
features and strategic contexts help to determine each group’s level of
success? In any system of separated powers, understanding the
bargaining process between the legislative and executive branches is
crucial to predicting policy outcomes and uncovering the determinants
of political power. In this article, we explore legislative-executive
conflict across the American states using a bargaining model that differs
from the models utilized in much of the existing literature. A new data
source on gubernatorial budget proposals and legislative enactments
provides the testing ground for our model’s empirical implications.
Efforts to assess the budgetary influence of legislators and chief
executives have traditionally relied upon setter or spatial models of
policymaking. In these models, the outcome of interbranch bargaining
is a function of the various players’ preferences, the order of interactions, and the location of status quo policy (Romer and Rosenthal 1978).
LEGISLATIVE STUDIES QUARTERLY, XXXIV, 1, February 2009
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Thad Kousser and Justin H. Phillips
Typically, the legislature is treated as a monopoly proposer, submitting
“take it or leave it” offers to an executive who possesses an absolute
veto. The executive is then forced to choose between the appropriations
figures contained in the bill and the reversionary or status quo point.
This reversion is almost always assumed to be the previous year’s
spending plan maintained, in the absence of legislative-executive agreement on a new budget, through a continuing resolution.
In spatial models, the legislature’s proposal power, combined with
its ability to credibly threaten to keep expenditures at the status quo
level, gives the legislature substantially greater influence over
budgetary outcomes than the executive holds. For instance, using a
spatial model of presidential-congressional bargaining, Kiewiet and
McCubbins (1988) have shown that when the president prefers smaller
expenditures than Congress proposes—the circumstance most favorable to the president—the president exerts only a limited influence
over budgetary outcomes. When the president prefers a higher level of
expenditures, the president has no influence at all. Kiewiet and
McCubbins’s insights have received additional support from a subsequent investigation by McCarty and Poole (1995).
In the study of American states, applications of setter models
also predict legislative dominance. In their influential analyses of state
budgeting under divided government, Alt and Lowry (1994, 2000)
amended the spatial model developed by Kiewiet and McCubbins to
account for the balanced-budget requirements that exist in most states.
In Alt and Lowry’s model, the legislature and governor must reach
agreement on fiscal balance (whether there is a surplus, deficit, or
balanced budget) in addition to fiscal scale. Alt and Lowry also added
an assumption, backed by Lowry, Alt, and Ferree’s (1998) empirical
work, that fiscal imbalance results in significant electoral losses for
the governor’s copartisans in the legislature.
Alt and Lowry’s model, like the Kiewiet and McCubbins model,
suggests executive weakness. When there is interbranch disagreement
over the size of the budget, the legislature can use its monopoly proposal
power to threaten the governor with fiscal imbalance by passing a
continuing resolution rather than a new budget. Since, in this model,
deficits or surpluses put the governor’s copartisans in the legislature
at risk, the governor will be forced to make significant concessions to
the legislature on fiscal scale in return for a balanced budget. After
reviewing the empirical predictions of their model under different fiscal
contexts and configurations of party control, Alt and Lowry concluded
that state legislatures are even stronger than Kiewiet and McCubbins
(1988) predicted Congress to be. Although governors can achieve some
Who Blinks First
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of their fiscal goals when members of their party control one or both
houses of the state legislature, chief executives must make severe concessions when they bargain with a legislature fully controlled by the
opposition party. When each party controls one branch, according to
Alt and Lowry (2000, 1043), “In no case does the governor achieve a
significant shift in the budget target in the direction of her ideal point.”
While spatial models and their progeny have unquestionably
provided important insights into legislative-executive bargaining, we
believe that these models are not the most appropriate simplification
of budgeting negotiations in most American states. Their portrayal of
gubernatorial weakness contradicts much of the existing scholarship
in the state politics literature. Case studies (Bernick and Wiggins 1991;
Gross 1991), surveys of political insiders (Abney and Lauth 1987;
Carey et al. 2003; Francis 1989), and other qualitative works (Beyle
2004; Rosenthal 1990, 1998, 2004) all point to the extraordinary power
of governors; many even refer to the governor as the “chief legislator.”
According to these analyses, governors can, and often do, dominate
the legislature with respect to the eternal question of how much to tax
and spend.
Additionally, the conclusion that the legislature can force the
governor to accept an unfavorable deal largely depends on the assumption that the reversion point in the absence of a budget agreement is
the status quo preserved through a continuing resolution. Continuing
resolutions, although frequent in federal budgeting (Fenno 1966;
Meyers 1997; Patashnik 1999), are not common or important considerations in state budget negotiations. Only nine states permit some
form of continuing resolution (Grooters and Eckl 1998), and even these
measures are labeled “minibudgets” (Connecticut), “interim budgets”
(New York), or “stopgap funding” (Pennsylvania). None can become
permanent, and the players in budget negotiations do not hope or fear
that they will avoid crafting a new budget.
We would argue that a late budget, with all of the political and
private costs that it entails, is the relevant reversion that drives
interbranch negotiations. In most states, a delayed budget triggers an
automatic shutdown of the government (Grooters and Eckl 1998). In
all states, it generates unfavorable press and usually a special legislative
session. Public polls conducted in California,1 New York,2 and New
Mexico3 have all demonstrated that a late budget cuts deeply into the
approval ratings of both branches. The possibility of voter disapproval
evens the field on which the budget bargaining game is played. Neither
branch likes a delayed budget agreement or a government shutdown,
so both sides face incentives to deal. The legislature’s proposal power
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Thad Kousser and Justin H. Phillips
erodes when legislators cannot fall back on an acceptable status quo.
This legislative limitation should make governors more powerful in
the budgetary process than spatial models predict, a dynamic suggesting
that an alternative model should be sought for describing state budget
making.
In this article, we offer an alternative simplification and discuss
our tests of its main implications. Our theory is based on formal models
devised by Rubinstein (1982, 1985) and Osborne and Rubinstein (1990)
and applied to state budget bargaining by Kousser (2005). Our model
treats the outcome of interbranch bargaining as a function of the institutional capacities and constraints of the legislature. We view budget
bargaining as a “staring match” in which the political and personal
costs of a delayed budget swamp the influences of proposal power and
status quo policies. Because the governor and legislature face shared
costs of delay, they both have incentive to reach an agreement quickly.
Negotiations are carried out informally, behind closed doors, rather
than in a sequence of bills sent to the governor’s desk. In the staringmatch dynamic that this negotiation creates, the identity of the “winner”
depends on relative levels of patience or endurance. Governors can
prevail in this game if they are willing to endure longer budget negotiations than their legislative opponents can stand.
In our application of the divide-the-dollar game, governors are
patient bargainers, but legislative patience is treated as a function of
professionalization. The governorship, in all states, is a full-time and
well-paid job; governors can afford to engage in long and protracted
negotiations over the budget. State legislatures, on the other hand, vary
widely in session lengths. Legislators receive sizable salaries to meet
nearly year-round in states such as California, New York, Illinois, Ohio,
and Massachusetts, but they meet as briefly as two or three months a
year in New Mexico, Georgia, Utah, and Kentucky and earn only small
salaries or per diems. Legislators in these less-professionalized
chambers usually hold second jobs to which they must return soon
after the legislative session. These individuals pay high opportunity
costs if their governor vetoes their budget and calls them in to a special
session. These costs make “citizen” legislators less patient, relative to
the governor and their counterparts in more-professionalized legislatures, and give the governor a bargaining advantage. Our staring-match
model predicts that the governor will be more successful when
bargaining with citizen, as opposed to highly professionalized, legislatures. And, since relatively few state legislatures are highly
professionalized, governors should generally be quite powerful in the
budgetary arena.
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Clearly we are not the first to argue that full-time legislatures
exert greater influence over budgetary matters than their part-time
counterparts, but our treatment of professionalization differs significantly from the models in much of the existing literature. Traditionally, professionalized legislatures—houses with longer sessions, higher
salaries, and plentiful staff support (King 2000; Squire 1992)—are
considered more powerful because they possess an increased intelligence capacity (Rosenthal 1990). These legislatures usually have a
large staff dedicated exclusively to fiscal policy, a revenue-estimating
capability independent of the executive branch, and a sizeable
contingent of experienced legislators. These features are believed to
reduce the governor’s traditional informational advantages and enhance
legislative independence and assertiveness (National Conference of
State Legislatures 2005). While professionalization may indeed have
these effects, we argue that its real advantage is that long sessions
make legislators willing to endure extended interbranch negotiations
over the size of the budget.
To test the predictions generated by our abstraction of the
budgeting process, we estimated an econometric model of the outcomes of interbranch bargaining over the size of the state budget. We
used an original dataset of annual gubernatorial budget proposals and
the corresponding legislative enactments culled from various issues
of the National Association of State Budget Officers’ Fiscal Survey of
States. We examined data for all states over a 16-year period, fiscal
years 1989 through 2004.
Our analysis revealed striking evidence of gubernatorial strength
in budgetary negotiations. Across all types of states and legislatures,
our econometric estimations show that the chief executive’s proposed
budget has a positive and statistically significant effect on the budget
that is ultimately passed and signed into law. Most important, we found
gubernatorial influence to indeed be inversely related to legislative
professionalization. Among states with citizen houses, there is nearly
a one-to-one relationship between the size of the gubernatorial proposal
and the size of the enacted budget. In states with professional legislative
bodies, the magnitude of gubernatorial influence falls by approximately
half. These results are consistent with the expectations of our staringmatch model and provide systematic empirical evidence that this
simplification of budget bargaining may be more appropriate for the
state context than the more traditionally utilized setter or spatial models.
In the next section, we present our staring-match model of state
budget bargaining in greater detail. We discuss the logic of the game,
its assumptions, and its predictions. Next, we present our estimation
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Thad Kousser and Justin H. Phillips
of an econometric model of the outcomes of legislative-executive
bargaining and interpret the results. We break down the components
of professionalism to show that professional legislatures are generally
more powerful than citizen houses, and it is longer sessions, rather
than higher salaries or more staff support, that grants them this power.
We then consider the potential endogeneity of gubernatorial budget
requests. We conclude by exploring the implications of our analysis
for the study of state politics.
Legislative and Gubernatorial Influence on State Budgeting
A Staring-Match Model of the Appropriations Process
To analyze the outcomes of legislative-executive bargaining over
the size of the budget, we applied the framework of the divide-thedollar games developed by Rubinstein (1982, 1985) and Osborne and
Rubinstein (1990). Our application of their games treats bargaining
between a governor and state legislature as a staring match; “blinking”
means signing or passing a proposal that closely reflects the demands
of one’s opponent. Hereafter, we refer to this treatment as “the staringmatch model.” The winner is determined largely by the relative patience
levels of the players, which, we argue, are functions of their institutional characteristics.
The game we describe here, like its spatial counterparts, is highly
stylized and abstract, lacking the detailed discussion of the appropriations process contained in many descriptive analyses of state budgeting
(cf. Garand and Baudoin 2004, National Association of State Budget
Officers 2002, and Rosenthal 2004). Yet this abstraction is useful for
conveying the logic of our argument in a simple, direct manner.
Furthermore, like the game’s basic intuition, many of the assumptions
made in the game conform nicely to budget bargaining at the state
level and are consistent with observations made by qualitative studies
and in interviews with legislative staff. A more-detailed discussion of
the assumptions necessary to apply the staring-match model to state
budget negotiations, along with proofs of the propositions we present,
can be found in Kousser’s work (2005, ch. 6).
Since this model has been used less frequently than spatial models,
we will review its assumptions and notation. There are two players, a
governor (PG) and a legislature (PL), each behaving as if it were a
unitary actor. Although this assumption ignores important intrabranch
divisions, each branch has rules for aggregating internal preferences
into a final position, justifying the common use of this assumption in
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models of interbranch bargaining (Alt and Lowry 2000; Cameron 2000;
Kiewiet and McCubbins 1988). When both the legislature and the
governor’s office are controlled by one political party, their disagreements may be fewer than under divided government. But because there
is still likely to be interbranch conflict, whether government is unified
or divided,4 we assumed that the branches must agree on how to “divide
the dollar” of the budget. The branch winning the biggest share of the
dollar (in the game) exerts the most control over the size of the state
budget (in our application of the game). The division of the dollar is
represented as an offer of (XL, XG), and XL can fall anywhere in the
interval [0, 1]. Rounds of play are numbered as T = {0, 1, 2, . . .}.
In the most natural application, the game begins with the legislature proposing how to divide the budget’s figurative dollar. (The
governor could also begin these informal negotiations and, as we later
demonstrate, the logic of the game would be the same and the division
of the dollar would remain largely unchanged.) Faced with this offer
of a budget with a given size, the governor either accepts and signs it
or sends the game into its next stage. The governor begins the second
stage with a counteroffer,5 but even if the legislature immediately
accepts it, the agreement has been delayed one round and both sides
receive a payoff that is discounted according to their patience levels.
The discount factor is conventionally denoted by δ.
Rounds of alternating offers continue until one player accepts
the other’s proposal. For every round that a bargain is delayed, the
utility a player receives from her or his portion of the dollar is equal to
that portion multiplied by δ. Assuming that this discount factor remains
constant from round to round, we would designate the value of an
agreement in round t to PL at the beginning of the game as XLδt. When
a player’s patience is set at δ = 0.9, the player will be indifferent between
receiving 45 cents in one round and getting 50 cents in the next, because
50 cents deflated by 0.9 gives the player 45 cents of utility.
We employed this deflation because continuing resolutions are
rare in states and a delayed budget deal is costly to both branches.6
When the players fail to adopt a state budget on time, the governor and
legislature’s public images are harmed. Even when the delay does not
run afoul of constitutional requirements, the failure to pass a new budget
is politically infeasible, because it denies the legislature and governor
the chance to create new programs and alter the composition of
spending. In either case, the status quo is a nonstarter, and the reversion point that dictates the players’ incentives is a delayed budget.
Each player is thus willing to give up some of the dollar to reach an
agreement early. Failure to reach any agreement is, of course, the worst
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Thad Kousser and Justin H. Phillips
possible outcome for both players, giving each zero utility. These
features characterize Rubinstein’s basic bargaining game.
Proposition 1. In a game satisfying all of these assumptions and where
both players face the same discount factor δ, there exists
a unique subgame perfect equilibrium.7 PL will always
propose the division (XL*, XG*) and accept an offer only
if it is better than or equal to YL*. Whenever it is PG’s
turn to make an offer, PG will propose (YL*, YG*) and
always accept an offer that matches or beats XG*. In
equilibrium, PL proposes (XL*, XG*) in round t = 0, and
PG accepts.
1
δ
( X L* , X G* ) = (
,
)
1+ δ 1+ δ
δ
1
(YL* , YG* ) = (
,
)
1+ δ 1+ δ
The proof of this proposition is outlined by Osborne and
Rubinstein (1990, 45) and traced out for the state politics application
by Kousser (2005, 233–37). Proposition 1’s implication for state politics
is that governors will not face a severe bargaining disadvantage because
they lack formal proposal power. In contrast with setter models, in
which gubernatorial power depends on a governor’s spatial preferences relative to the legislature’s and the level of fiscal imbalance in a
state,8 in our version of Rubinstein’s model, governors can receive
some of what they want no matter which direction they wish to move
the size of government and no matter who begins the bargaining.
Regardless of which branch makes the first offer, power over the budget
will be divided quite equitably. Both branches bargain in the shadow
of a late budget and the political penalties it can bring. Both are eager
to avoid delay, and whichever branch can move first makes a fair offer
that it knows the other branch can afford to accept. In the most straightforward application of the staring-match model, this offer comes when
the legislature passes a budget bill. But even if the governor’s public
budget proposal, which often receives much media attention and sets
the agenda for later negotiations, is thought of as the first offer, the
theoretical prediction for the division of the dollar does not change
radically. As the payoffs demonstrate, the “first mover” advantage that
accrues to the branch making the initial offer is small when both players
are relatively patient, and not tremendously large even when they are
in a hurry to pass a budget. When both players discount payoffs that
are delayed one round by a factor of 0.9, the first mover receives 52.6
Who Blinks First
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cents of the dollar and the other branch gets 47.4 cents. Even when the
discount factor equals 0.7, the division of the dollar is still a somewhatequitable 58.8 cents to 41.2 cents. Regardless of which branch is
thought of as making the first offer, Proposition 1 leads to the following
empirical implication:
Hypothesis 1: Governors will exert a powerful influence over the size
of state budgets.
Varying Legislative Patience
The basic model assumes that governors and legislators possess
the same patience level, but this assumption may not always hold true.
In particular, the members of a citizen legislature should be significantly less willing to engage in protracted budgetary disputes with the
governor than their more-professionalized counterparts. The rationale
here is that, in addition to political costs that both branches pay when
there is budgetary gridlock, lawmakers serving in a lessprofessionalized legislature face private costs of delay. These costs
will decrease the legislature’s patience and advantage the governor.
There are, of course, several relatively professionalized state
legislatures. These chambers resemble the U.S. House of Representatives: they meet in lengthy sessions, their members are well paid, and
the legislature employs numerous nonelected staff. In states such as
California, New York, and Michigan, there are few, if any, restrictions
on the number of days the legislature may meet; as a result, lawmakers
are in session much of the year. Furthermore, legislators serving in
these chambers receive annual salaries in excess of $75,000, as well
as generous per diems (Council of State Governments 2005). These
lawmakers can therefore treat legislative service as a career and do not
need second jobs, even as the session length makes holding a second
job close to impossible.
Most state legislatures, however, are notably less professionalized.
In these chambers, the number of days that legislators are allowed to meet
is often constitutionally restricted. On average, regular sessions are limited
to approximately 90 calendar days per year; in extreme cases, sessions are
constrained to no more than 60 or 90 days biennially. Compensation for
service in most chambers is also low or nonexistent. To support themselves and their families, legislators in citizen chambers usually hold
second jobs to which they must return soon after the legislative session.
As a result, members of a part-time body face high opportunity
costs when they fail to reach agreement on a budget with the governor.
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Thad Kousser and Justin H. Phillips
In the absence of such an agreement, legislators are usually forced
into what may be a time-consuming special session and are prevented
from pursuing their private careers or personal lives. The prospect of
leaving their day jobs to resolve budget conflict should make members
impatient. On the other hand, governors pay much lower private costs
when they veto a bill at the end of a session. They may force a special
session, stalling whatever private, travel, or governing plans they might
have,9 but because all governors are paid well to do their job fulltime,10 they can endure round after round of negotiations. Participants
in gubernatorial negotiations with the less-professional legislatures
point out the paramount importance of this dynamic. A senior advisor
to Oregon Governor John Kitzhaber explained that, “As session goes
on, the wait is in our favor.”11 In New Mexico, a special session called
by Governor Gary Johnson to resolve the 2000 budget standoff led
legislators to grouse, take political heat, and ultimately accede to many
of the governor’s demands.12 We therefore expected professional
chambers to be able to match the governor’s endurance, whereas parttime bodies would be vulnerable to threats of a veto and extended
negotiations. One piece of descriptive evidence consistent with our
expectation is that budget standoffs, although rare, occur primarily in
professional, full-time legislatures.13
This potential asymmetry in the patience levels that the branches
possess can be formalized in an extension of the basic Rubinstein model
in which the two players have different discount rates. When a citizen
legislature negotiates, δL will be lower than δG. If the governor’s
advantage in patience is large, then it will swamp the advantage that
the legislature holds from moving first. Proposition 2 is simply a lessgeneral form of Proposition 1 in which discount rates are allowed to vary.
Proposition 2. In a game similar to the basic game but where players
face individual discount factors δL and δG, there exists
a unique subgame perfect equilibrium. PL will always
propose the division (XL*, XG*) and accept an offer only
if it is better than or equal to YL*. Whenever it is PG’s
turn to make an offer, PG will propose (YL*, YG*) and
always accept an offer that matches or beats XG*. In
equilibrium, PL proposes (XL*, XG*) in round t = 0, and
PG accepts.
1 − δ G δ G (1 − δ L )
( X L* , X G* ) = (
,
)
1 − δ Gδ L 1 − δ G δ L
(YL* , YG* ) = (
δ L (1 − δ G ) 1 − δ L
,
)
1 − δ Gδ L 1 − δ G δ L
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FIGURE 1
Payoffs for Legislatures with Different Levels of Patience
0.6
Le gis la ture makes
M a ke s initial
Initia l P
ro po s a l
Legislature
proposal
0.4
0.3
0.2
Legislature’s Share of the Dollar
0.5
Go ve rno rmakes
M a ke sinitial
Initia lproposal
P ro po s a l
Governor
0.1
0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Legislature’s Patience Level
The exact payoffs that the legislature receives at different levels
of legislative patience are displayed graphically in Figure 1. One can
see how steeply the legislature’s share of the dollar drops as its members
become less and less patient, assuming that the governor has a discount
rate of δ = 0.9. Although we do not investigate variations in the
governor’s patience level here, it likely changes with such factors as
approval ratings, the timing of the next election, and the governor’s
political ambitions. Investigations of these variables may provide
fruitful further tests of the implications of the model.
The solid line maps the payoffs when the legislature begins the
bargaining, and the dotted line shows the results if the governor moves
first. The gap between these lines—the first-mover advantage—is
relatively narrow. What really determines who will control the budget
is the legislature’s patience. A professional legislature that can credibly
threaten to wait the governor out in a special session will win, gaining
52.5 cents if it is equally as patient as the governor. If the private
demands on members of a citizen legislature reduce their discount
rate to 0.5, then they will get a mere 18.2 cents, even when they move
first. Hypothesis 2 states the specific, testable implication of this
theoretical finding.
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Thad Kousser and Justin H. Phillips
Hypothesis 2: The influence that governors exert over the size of their
state budgets will grow as the level of legislative
professionalism in their states declines.
Before discussing the tests our hypotheses, it is worth noting
that the centrality of patience or discount rates in our model is one of
the features that most clearly distinguishes it from existing analyses.
Spatial approaches to legislative-executive bargaining, at both the
national and state levels, rarely consider the potential effects that shifts
in discount rates may have on outcomes (Alt and Lowry 1994, 2000;
Kiewiet and McCubbins 1988; McCarty and Poole 1995; but see Banks
and Duggan 2006). Even when the patience levels of the players are
allowed to vary, spatial models predict no effect. Primo (2002), for
instance, examined how some of these dynamics might affect Romer
and Rosenthal’s (1978) model. He found that, even when spatial models
are extended to multiple stages of bargaining, discount rates do not
factor into the equilibrium. Primo’s results suggest that impatient citizen
legislatures should not face a bargaining disadvantage because
“impatience and time preferences may not be key features of political
bargaining” (421).
Testing Predictions of the Staring-Match Model
We tested our hypotheses by systematically examining the
relationship between the size of the governor’s proposed increase in
total per capita state expenditures (measured as a percentage of the
previous year’s budget) and the size of enacted spending change—
that is, the change contained in the budget ultimately adopted by the
legislature and signed into law (again measured as a percentage of the
prior year’s budget).14 Unlike most of the existing literature, our study
gauges gubernatorial power by directly measuring the policy preferences of governors, rather than assuming that their party affiliations
tell us exactly what they want.
Prior studies of variation in state policy outputs that relied
exclusively on measures of party control as a proxy for gubernatorial
and legislative preferences gained their causal traction from the
assumption that Democrats always and everywhere want government
to expand, or that the magnitude of policy disagreements between the
two major parties is constant across states (Alt and Lowry 2000; Dye
1966, 1984; Garand 1988; Hofferbert 1966; Kousser 2002; Smith 1997;
Winters 1976; but see McAtee, Yackee, and Lowery 2003 for a relaxation of the latter assumption). None of these studies has found that
Who Blinks First
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governors can move policy in their preferred direction in a statistically significant manner.
Instead of using party affiliations as a proxy, we chose to measure
executive preferences directly, by recording the percentage change in
per capita expenditures that the governor proposes at the beginning of
the year. Our choice is similar to Clark’s (1998) strategy of gathering
gubernatorial recommendations for agency budgets from 20 states and
Canes-Wrone’s (2001) use of presidential budget proposals to analyze
federal bargaining. A dataset that systematically gathered legislative
budget proposals would be ideal, but no such dataset exists. Still, using
gubernatorial proposals for spending changes as an independent
variable and legislative enactments as our dependent variable, we can
see how far different types of legislatures shift policy from what the
governor wants. This is the same empirical strategy that Kiewiet and
McCubbins (1988) employed in their influential study of presidentialcongressional bargaining.
As mentioned previously, we collected our dataset of gubernatorial budget proposals and enacted state budgets from various issues of
The Fiscal Survey of States, a publication of the National Association
of State Budget Officers (NASBO). Each year, NASBO conducts two
surveys of state budget officials to identify trends and changes in state
fiscal policy. The spring survey gathers information concerning the
governor’s proposed general-fund budget, and the autumn survey identifies details of the enacted budget (usually Table A-3 in both reports).
Our analysis includes data for all states over a 16-year period, fiscal
years 1989 through 2004. Data prior to fiscal year 1988 are unavailable. Since NASBO consistently reports data in current dollars, we
converted the values for each year into 2000 dollars using the Consumer
Price Index for all urban consumers (CPI-U).
Evaluating the predictive power of the staring-match model also
required us to identify an appropriate measure of the professionalization
of state legislatures. A venerable literature in state politics has demonstrated the importance of variation in legislative salaries, session
lengths, staff support, and other resources (Berry, Berkman, and
Schneiderman 2000; Fiorina 1994; Hamm and Moncrief 2004; Karnig
and Sigelman 1975; Roeder 1979; Squire and Hamm 2005; Thompson
1986). Many researchers follow Squire (1992, 72) or King (2000, 329),
combining the components of legislative professionalism into a single
index. To be consistent with the existing literature, we began with this
approach. We employed the widely used trichotomous categorization
developed by the National Conference of State Legislatures (NCSL),
as well as Squire’s (1992) continuous index. Both measures are based
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Thad Kousser and Justin H. Phillips
upon the length of time that legislators spend in session, the amount of
their total compensation, and the number of legislative staff members.
Because levels of state professionalism changed dramatically from the
1960s through the 1980s but were relatively stable during our period
of study, both of our measures are constant in each state across time.
We recast the NCSL’s red, white, and blue categories as “professional,”
“semiprofessional,” and “citizen.” As a report by the NCSL (2005)
details, lawmakers in professionalized bodies dedicate much more time
to legislative service, earn about four times as much, and work with
eight times as many staff members as their counterparts in citizen
legislatures.
We began our analysis by examining, for each type of legislature,
the bivariate relationship between the governor’s proposed budget and
the enacted budget in all states, with three exclusions. We excluded
Alaska and Wyoming because they both rely heavily upon severance
taxes on natural resources. The use of severance taxes results in fairly
dramatic year-to-year variation in tax revenues and thus expenditures.
These variations are driven largely by the global commodities market,
as opposed to the budgetary choices of legislators and governors
(Matsusaka 2004). We also excluded Nebraska because of its nonpartisan legislature.
Our preliminary results, reported in Table 1, are entirely consistent
with the hypotheses derived from the staring-match model. Across all
three categories of legislatures, the coefficient on the variable that
measures the size of the gubernatorial budget proposal is positive and
statistically significant at the 99% level. This result provides support
for our contention that governors are consistently powerful in the
budgetary arena (Hypothesis 1). As predicted in Hypothesis 2, the
magnitude of the effect is inversely related to legislative
professionalization. Among citizen bodies, this coefficient is 0.86.
Substantively, this finding indicates that when a governor negotiating
with a citizen legislature proposes increasing the budget by 1%, the
final enacted budget should increase by 0.86%. A proposed cut in
spending—although empirically much rarer—would bring an
analogous decrease in the size of the budget. When a governor
negotiates with a more-professional legislature, however, the governor’s
power to dictate fiscal outcomes declines. A proposed change of 1%
leads to an enacted increase of only 0.73% when the governor negotiates
with a semiprofessional legislature and a 0.46% change when the
governor faces a professional house. These differences, we will later
show, are statistically meaningful and still present when other factors
are held constant. Furthermore, among states with citizen legislatures,
Who Blinks First
69
TABLE 1
Governor’s Influence by Type of Legislature
Variables
Citizen
Semiprofessional
Legislatures
Legislatures
Governor’s Proposal
.86**
(.05)
.73**
(.08)
Professional
Legislatures
.46**
(.07)
All
Legislatures
.90**
(.07)
Squire’s Index of
Professionalism (1992)
__
__
__
.01
(1.68)
Governor’s Proposal × Squire’s
Index of Professionalism
__
__
__
–.83**
(0.20)
1.34**
(.29)
1.98**
(.44)
1.57**
(.56)
1.62**
(0.46)
Constant
N
R2
286
.48
370
.18
178
.19
852
.25
Note: Table entries are regression coefficients. In the “All Legislatures” model, the estimated
coefficient for a governor’s proposal represents the effect that this variable would have in a
hypothetical legislature with a “0” level of professionalism, measured on Squire’s scale.
**p < .01.
the governor’s budgetary proposal alone explains almost half of the
variation in outcomes, but among states with more-professionalized
legislative bodies, the governor’s proposal accounts for less than 20%
of the variation in outcomes.
The last column in Table 1 combines data from all three types of
legislatures with an interaction testing the hypothesis that a governor’s
proposal will have a smaller effect on the final budget outcome when
the legislature is more professional according to Squire’s (1992)
continuous measure. This interaction effect is strongly significant in
the expected direction. To interpret it, we obtained the effect of a
governor’s proposal when the legislature has a given level of professionalism; we added the product of that given level and the interaction
coefficient (–0.83) to the estimated coefficient of a governor’s proposal
alone (0.90). The results indicate that a governor’s proposed 1%
increase in the size of the budget should translate into a 0.88% increase
in spending when the governor negotiates with a legislature like Utah’s,
which is one standard deviation below the mean level of professionalism,
but only a 0.62% increase when the state’s legislative professionalism
registers one standard deviation above the mean, as in Pennsylvania.
70
Thad Kousser and Justin H. Phillips
Altogether, these results provide preliminary evidence that governors,
while powerful, are less influential in the face of legislatures that meet
in long sessions, pay high salaries, and provide staff support.
The results reported in Table 1 may of course reflect the influence
of omitted variables. We addressed this problem by conducting a
multiple regression analysis that included a number of potentially
influential political and economic variables. The first of these variables
is the partisan composition of the legislature. Existing research in state
politics has found evidence, albeit weak and oftentimes conditional,
that Democratic control of the legislature leads to a larger state public
sector and larger year-to-year increases in expenditures or revenues
(Alt and Lowry 1994, 2000; Phillips 2005). To allow for this possibility,
we employed a continuous measure of the legislative strength of the
Democratic Party in each state, calculated as the weighted percentage
of Democrats serving in both the state’s lower and upper legislative
chambers, recorded from appropriate editions of the Council of State
Governments’ Book of the States. Smith (1997) has identified this
approach as the most appropriate method for capturing the partisan
makeup of state government. Additionally, we accounted for crosssectional variations in the timing of state budget processes to ensure
that our measure accurately reflects the partisan composition of the
legislature at the time the budget was passed and signed into law.
Because empirical exploration indicated that the power of governors
was not contingent upon the presence of divided government,15 we did
not include any measures of divided government in the models
presented. Similarly, although the results of an unreported analysis
showed that governors who possess more institutional budget powers
according to Beyle’s (2004) index exert more influence over fiscal
changes, we omitted this test from our final models, because the effect
fell short of statistical significance and would have required threeway interactions. We also omitted from the final reported models
potential control factors such as whether a state had an annual or
biennial budget and whether or not it allowed continuing resolutions.
These factors (either entered alone or in interaction with a governor’s
proposal) did not have large or statistically significant effects, and
their exclusion did not substantially change the estimated effects of
our key variables of interest.
Previous research has also shown that economic factors are
important determinants of state budgetary policy (Dye 1966, 1984;
Winters 1976). We allowed for these influences by utilizing, as independent variables, per capita income (measured in thousands of dollars)
and the state-level unemployment rate (both taken from the U.S. Census
Who Blinks First
71
Bureau’s Statistical Abstract of the United States). To control for the
possibility that state expenditures increase during election years (that
is, the presence of a political business cycle), we included a dummy
variable for years in which lawmakers must run for reelection, as
reported in the Book of the States. Finally, following Phillips’s (2005)
work, we also included the previous year’s per capita expenditures, as
reported in NASBO records, to control for status quo fiscal policy and
a lagged measure of the state’s budget surplus or deficit (measured as
a share of the total budget).
All of our econometric estimations also utilize year and state
fixed effects. The year fixed effects control for common shocks that
affect all states in a given year, such as changes in the national or
global economy or changes in the national political environment. The
state fixed effects capture all relevant variables that are idiosyncratic
to individual states or that remain unchanged over the time period of
our analysis, such as culture, voter ideology, and political institutions.
Of particular relevance, fixed effects control for other features of the
bargaining environment that may enhance a governor’s ability to prevail
in battles over the size of the state budget, such as the item veto and
gubernatorial impoundment powers.
The first of our multiple regression results are reported in Table 2.
Model 1 is a baseline estimation that includes all states but does not
account for cross-sectional variation in legislative professionalization.
As in Table 1, the coefficient on the governor’s proposal is positive
and statistically significant, indicating that governors are powerful
actors in the budgetary arena even after one controls for a number of
potentially confounding influences. Model 2 is a direct test of our
second hypothesis. Here the governor’s budgetary proposal is interacted with two dummy variables: one for the existence of a semiprofessional legislature and the other for a citizen body. The reference
category in this regression is professional legislatures. We did not
include separate dummy variables for each legislative type in the
equation because we used fixed effects, which account for the
independent effect of professionalization.16
This new estimation provides the strongest evidence yet for the
staring-match model. Once again, the size of the governor’s proposed
budget has a significant and positive effect on the size of the budget
ultimately adopted by the legislature and signed into law. Most
important, the coefficients on the interaction terms are also positive
and significant at the 99% level: the effect of the gubernatorial proposal
on the final budget increases in a statistically meaningful fashion as
the professionalization of the legislature declines. When the governor
72
Thad Kousser and Justin H. Phillips
TABLE 2
Governor’s Influence by Type of Legislature,
Full Multiple Regression
Model 1
Model 2
Model 3a
(Squire 1992)
Model 3b
(Squire 2007)
.50**
(.05)
.29**
(.07)
.80**
(.08)
.92**
(.09)
Governor’s Proposal ×
Semiprofessional Legislature
__
.25*
(.10)
__
__
Governor’s Proposal ×
Citizen Legislature
__
.49**
(.11)
__
__
Governor’s Proposal × Squire’s
Index of Professionalism
__
__
–.96**
(.21)
–1.05**
(.23)
Squire’s Index of Professionalism
__
__
__
5.32
(8.20)
Variables
Governor’s Proposal
% Democrat Legislature
–.06
(.05)
–.07
(.05)
–.07
(.05)
–.08
(.05)
Lagged Expenditures per Capita
–.01**
(.002)
–.01**
(.002)
–.01**
(.002)
–.00
(.00)
Legislative Election Year
1.38
(.77)
1.56*
(.76)
1.58*
(.76)
1.91**
(.83)
Unemployment Rate
–.48
(.36)
–.56
(.36)
–.58
(.36)
–.66
(.40)
Personal Income per Capita
1.01**
(.37)
1.02**
(.40)
1.01**
(.37)
.63*
(.38)
% Lagged Surplus
.07
(.06)
.03
(.06)
.03
(.06)
.03
(.07)
–5.96
(9.49)
–4.02
(9.39)
–3.15
(9.39)
–7.15
(10.31)
787
787
787
787
.30
.32
.32
.30
Constant
N
R
2
Note: Estimated using state and year fixed effects.
**p < .01; *p < .05.
Who Blinks First
73
negotiates with a professional legislature, an executive proposal for
an additional 1% increase in state spending per capita leads to only a
0.29% change in the legislature’s enacted budget, all else being equal.
When the legislature is semiprofessional, this marginal effect grows
to 0.54%. It rises to 0.78% when the governor negotiates with citizen
legislators.
Tests also show that the difference between the coefficients on
our two interaction terms is itself statistically significant at the 95%
level: not only does the governor get significantly more of what she or
he wants when the chamber shifts from professional (our baseline
category) to citizen or semiprofessional, but the governor also gets
significantly more of what she or he wants when the chamber shifts
from professional to semiprofessional. These findings are consistent
with the bivariate results shown in Table 1, as well as with the results
of Models 3a and 3b, which use Squire’s (1992) continuous measure
of legislative professionalism and Squire’s (2007) updated, dynamic
measure17 rather than the trichotomous categorization. Again the
statistically significant coefficient on the interaction between a
governor’s proposal and the level of professionalism suggests that chief
executives have less power when bargaining with full-time legislatures.
Thus far we have discussed a measure of legislative
professionalization that aggregates the various components of this
concept—session length, compensation, and staff—into a single indicator. While there are strong theoretical and empirical reasons for this
aggregation, the staring-match model makes a prediction about which
of these components matter. In particular, it suggests that session length
is the primary factor affecting the legislature’s patience and thus the
governor’s power. By contrast, our application of the staring-match
logic does not predict that increased staffing will affect the balance of
power between the branches because it affects a legislature’s informational capacity rather than its patience. High salaries, which can free
legislators from other obligations, might also affect patience, but
members of houses that regularly meet for full-time sessions should
exhibit the highest levels of patience. To examine this claim and to
further explore the relationship between legislative structure and
bargaining outcomes, we replaced our summary measures with the
separate components of professionalism. The first measure records a
legislature’s level of compensation, including both base salary and per
diem expenses. The second measures session length, in legislative days
per biennium, and the third reflects the ratio of staff per legislator.
Because staff, salary, and session length are not perfectly collinear,18
we estimated their separate effects.
74
Thad Kousser and Justin H. Phillips
The results reported in Table 3 explore the effects of each of
these components of legislative professionalism on gubernatorial
power. Models 4, 5, and 6 estimate the influence of each component
separately, and Model 7 tests their combined effects. Whether analyzed
separately or together, the story is the same: session length provides
the link between professionalism and executive power, with the
governor exerting less control over the budget when a house meets for
longer and longer sessions. The interaction between total session days
and a governor’s proposal is statistically significant in the expected
direction, but changes in the other two components do not significantly alter the effect of executive proposals. To gauge the scale of the
effect of session length, we calculated the effect, according to the results
in our fully specified Model 7, of shifting this variable from one
standard deviation below its mean to one deviation above its mean.
Consider two legislatures, both of which pay the average salary in our
sample (about $25,000 a year), provide average staffing levels (3.5
assistants per legislator), and exhibit similar values on all of the control
variables. If one of these legislatures meets for 66 days over a twoyear period (a typical session for North Dakota’s legislature), then
every extra percentage-point increase in spending proposed by the
governor translates into a 0.78% increase in the enacted budget. If a
house is similar in all other respects but meets for 263 days per
biennium, as Wisconsin did for 1997 and 1998, then a change of 1% in
the governor’s proposal yields only an estimated 0.59% change in the
budget that the legislature finally enacts. This finding is consistent
with our conjecture that a full-time house has the patience to strengthen
its bargaining position against the governor, thus supporting the logic
of the staring-match model.
The results presented up to this point suggest that interbranch
negotiations over the size of the state budget are better conceptualized
using a staring-match model than with a setter model. Nevertheless,
one potential counterclaim is that the setter model may still be appropriate for the handful of states in which continuing resolutions are
allowed. We do not see, however, why this would be true. Remember
that continuing resolutions in those states that allow them are (at best)
short-term solutions—none can become permanent. Furthermore, their
use does not insulate the governor and legislature from the high political
and personal costs associated with a late budget. Continuing resolutions
operate quite differently at the national level. Continuing resolutions
are used by Congress and the president on well over half of all budget
bills, and they can be utilized for many months or longer (Meyers
1997). In fact, President Clinton and the Republican-controlled
Who Blinks First
75
TABLE 3
Governor’s Influence by Each Component
of Legislative Professionalism
Variables
Model 4
Model 5
Model 6
Model 7
Governor’s Proposal
.52**
(.07)
.83**
(.08)
.56**
(.07)
.80**
(.08)
Salary (in $1,000s)
.02
(.04)
__
__
.04
(.04)
Governor’s Proposal × Salary
–.001
(.002)
__
__
.005
(.003)
Session Length (hundreds of days)
__
–.002
(.53)
__
–.06
(.53)
Governor’s Proposal × Session Length
__
–.12**
(.02)
__
–.13**
(.02)
Staff per Member
__
__
–.19
(.43)
.02
(.45)
Governor’s Proposal × Staff per Member
__
__
–.01
(.01)
–.017
(.014)
% Democrat Legislature
–.06
(.05)
–.07
(.05)
–.06
(.05)
–.07
(.05)
Lagged Expenditures per Capita
–.01**
(.002)
–.01**
(.002)
–.01**
(.002)
–.01**
(.002)
Legislative Election Year
1.38
(.77)
1.64*
(.78)
1.44
(.77)
1.71*
(.76)
Unemployment Rate
–.47
(.36)
–.59
(.33)
–.51
(.36)
–.60
(.36)
Personal Income per Capita (in $1,000s)
1.04**
(.38)
.97**
(.36)
1.03**
(.37)
1.01**
(.37)
% Lagged Surplus
.06
(.64)
.02
(.06)
.06
(.06)
.01
(.06)
–6.83
(9.60)
–1.98
(9.36)
–5.39
(9.65)
–3.55
(9.76)
787
.30
787
.33
787
.30
787
.34
Constant
N
R2
Note: Estimated using state and year fixed effects.
**p < .01; *p < .05.
76
Thad Kousser and Justin H. Phillips
Congress, unable to reach a budget agreement in fiscal year 1999,
funded the federal government for the entire year using a series of
seven continuing resolutions (Davidson, Oleszek, and Lee 2007).
Still, we empirically tested whether or not the availability of
continuing resolutions affects gubernatorial power in Model 8. To do
so, we reestimated Model 5, this time including an interaction between
the governor’s budgetary proposal and a dummy for whether or not
continuing resolutions are expressly allowed. The results are presented
in Table 4. We found no meaningful effect. The coefficient on the new
interaction terms is negative and small in magnitude, and it fails to
even approach statistical significance. Because some states have no
legal provision regarding the use of continuing resolutions and no test
case (because of the lack of late budgets), we also made use of several
alternative codings. Specifically, we created dummy variables for states
where continuing resolutions may be allowed (those where continuing
resolutions are expressly authorized and those where there is no
provision regarding their use), states where a late budget triggers a
full government shutdown, and states where a late budget triggers either
a full or partial shutdown.19 These dummy variables are used in Models
9 through 11, with the results also shown in Table 4. Again we found
no effect. In each of these estimations, the interaction between gubernatorial budget proposals and legislative session length (the most
theoretically relevant component of legislative professionalization)
remains negative and statistically significant, providing additional
evidence that staring-match models are preferable to setter models for
conceptualizing state budget bargaining.
The Potential Endogeneity in Gubernatorial Budget Requests
It is also important to consider the extent to which state chief
executives have an incentive to misrepresent their preferences when
they submit their proposed budgets and whether or not this misrepresentation would bias our econometric results. Governors may, for instance, foresee legislative strength and adjust their budgetary proposals
accordingly. According to this logic, governors facing professional
legislatures would weaken their initial offers, moving closer to their
legislatures’ ideal points. Governors negotiating with citizen
legislatures would have no incentive to adjust their proposals, since
they should be able prevail in budgetary negotiations, given the institutional weakness of citizen legislatures.
We doubt, however, that governors frequently “game” their
budgetary proposals in this manner. When governors present their
Who Blinks First
77
TABLE 4
Governor’s Influence by Legislative Professionalization
and Status Quo Point
Variables
Model 8
Model 9
Model 10
Governor’s Proposal
.81**
(.08)
.85**
(.08)
.83**
(.09)
.81**
(.09)
Session Length (hundreds of days)
–.02
(.53)
–.03
(.53)
–.02
(.53)
–.01
(.53)
Governor’s Proposal × Session Length
–.10**
(.03)
–.10**
(.02)
–.12**
(.02)
–.12**
(.02)
Governor’s Proposal × Continuing
Resolutions Allowed
–.14
(.14)
__
__
__
Governor’s Proposal × Continuing
Resolutions May Be Allowed
__
–.15
(.10)
__
__
Full Shutdown
__
__
.002
(.10)
__
Full or Partial Shutdown
__
__
__
.03
(.09)
% Democrat Legislature
–.07
(.05)
–.07
(.05)
–.07
(.05)
–.07
(.05)
Lagged Expenditures per Capita
–.01**
(.002)
–.01**
(.002)
–.01**
(.002)
–.01**
(.002)
Legislative Election Year
1.63*
(.76)
1.64*
(.76)
1.65*
(.76)
–1.64*
(.76)
Unemployment Rate
–.56
(.35)
–.57
(.35)
–.55
(.35)
–.55
(.35)
Personal Income per Capita (in $1,000s)
.97**
(.36)
1.00**
(.36)
.97**
(.37)
.97**
(.37)
% Lagged Surplus
.02
(.06)
.02
(.06)
.02
(.06)
.02
(.06)
–1.94
(9.36)
–2.04
(9.36)
–1.99
(9.38)
–2.06
(9.37)
787
.33
787
.33
787
.33
Constant
N
R2
787
.33
Note: Estimated using state and year fixed effects.
**p < .01; *p < .05.
Model 11
78
Thad Kousser and Justin H. Phillips
budgets, they send a signal to voters, interest groups, and campaign
contributors about their governing philosophy and legislative priorities.
Surely governors realize the signaling role of their actions and make
proposals accordingly. Moreover, the public is not likely to understand or appreciate complicated strategies, and officials may not be
able to explain them effectively. Such considerations should attenuate
any impulse the governor may have to game the proposed budget
(Denzau, Riker, and Shepsle 1985).
If, however, governors facing highly professionalized legislatures
do systematically move their initial budget proposals closer to their
legislatures’ ideal points, then the observed effect should be a stronger
relationship between executive proposals and enacted budgets. In other
words, the possibility of strategic misrepresentation should bias our
results against finding that governors are less powerful in states with
professionalized legislatures. This result is, of course, the opposite of
what our econometric estimations actually reveal. We have strong
evidence that governors are least powerful in states with these
legislatures. Thus, we are even more confident that the insight provided
by the staring-match model is correct and that initial gubernatorial
budget proposals are sincere.
To further satisfy skeptical readers, we empirically tested for the
possibility that gubernatorial budgetary proposals are gamed. These
results appear in the Appendix, available on the LSQ website <http://
www.uiowa.edu/~lsq/KousserPhillips_Appendix>. We examined
whether or not Democratic and Republican governors systematically
alter the size of their proposals when facing professional (that is, strong)
legislatures controlled by the opposition party. We did not uncover
any evidence suggesting that governors’ initial offers are shaped by
the strategic situations they face. Thus, we are confident that executive
budgetary proposals are not endogenous to states’ institutional
configurations.
Conclusion and Implications
Attempts to assess the budgetary influence of state legislators
and governors have traditionally relied upon spatial or setter models
of policymaking imported from studies of the U.S. Congress. In these
models, legislators, through their monopoly on proposal power and
their ability to credibly threaten to keep expenditures at the status quo
level, have substantially greater influence on budget making than
governors wield. This result contradicts numerous qualitative analyses
in the state politics literature that find that governors are the chief
Who Blinks First
79
legislators in the budgetary arena. We proposed and tested an alternative
simplification of state budgeting modeled on the games developed by
Rubinstein (1982, 1985) and Osborne and Rubinstein (1990) and
applied to states by Kousser (2005). Governors are quite potent in this
staring-match model, and the power of legislators declines when shorter
sessions or lower salaries make them impatient and willing to make a
deal.
Using an original dataset of gubernatorial budget proposals and
legislatively enacted state budgets, we explored the model’s predictions.
Overall, we found striking evidence of gubernatorial influence. Our
econometric estimations show that, across all types of legislatures, the
chief executive’s proposed budget has a positive and statistically
significant effect on the budget that is ultimately passed and signed
into law. Most important, however, the influence of legislators is closely
linked to levels of legislative professionalism. Legislators can drive a
particularly hard bargain when they typically hold long sessions; session
length is the component of professionalism most closely linked to the
theoretical concept of bargaining patience. This empirical relationship may be driven by something other than variation in patience: fulltime legislators might acquire more policy knowledge or political
acumen during their longer sessions, or full-time work might attract a
different type of legislator. Still, this finding is consistent with our
theoretical model and shows the importance of separating the session
length component from the staff and salary components. Just as Alt
and Lowry’s (1994, 2000) work demonstrated the centrality of state
institutions and party control in determining fiscal outcomes, our
empirical analysis reveals the power of governors and the important
mediating effect of legislative professionalism.
We believe that these findings yield three more-general lessons
for the study of bargaining between governmental branches. First, when
researchers apply formal models of bargaining, one size does not fit
all legislatures. Although setter models may capture the key dynamics
of federal budget bargaining in the U.S. Congress, where a continuing
resolution is a realistic reversionary outcome, these models do not
appear to fit well with states that demand that a new budget be passed
every year. Second, while variation in legislative professionalism
clearly determines legislative power, it is session length—more than
salary or staff—that appears to drive this trend. Isolating the theoretically distinct components of legislative professionalism can yield new
insights into this variable’s importance. Finally, directly measuring
governors’ preferences, rather than inferring them from party affiliations, demonstrates the significant influence that these preferences exert
80
Thad Kousser and Justin H. Phillips
over state policy. Overall, by closely examining the way that institutional contexts shape the strategies available to political actors, we
can uncover links between rules, political reforms, and bargaining
outcomes that may have implications for broader comparative studies.
Thad Kousser <tkousser@ucsd.edu> is Associate Professor of
Political Science, 9500 Gilman Drive, #0521, SSB 369, University of
California, San Diego, La Jolla, CA 92093-0521. Justin H. Phillips
<jhp2121@columbia.edu> is Assistant Professor of Political Science,
IAB 7th Floor, 420 West 118th Street, Columbia University, New York,
NY 10027.
NOTES
1. The time series of legislative and gubernatorial approval in California reported
by the Field Poll reveals how severe these penalties can be. In the first two years of
Governor Gray Davis’s administration, 1999 and 2000, the branches reached budget
deals before the start of the new fiscal year. During Davis’s last two years, 2002 and
2003, negotiations dragged into September and August (Wilson and Ebbert 2006, 276).
In 1999 and 2000, the governor’s and the legislature’s approval ratings remained
essentially constant over the summer. But the legislature’s approval ratings dropped
from 45% to 35% from July to September of 2002 and from 31% to 19% from April to
July of 2003 (Field Poll 2004, 2). Davis’s already-low ratings edged downward as well
in each of those summers (Field Poll 2003, 3).
2. When the 2001 budget deal in New York was delayed, 84% of survey respondents were “very concerned” or “somewhat concerned” about the budget, and 63%
blamed both Governor Pataki and the state legislature (Quinnipiac 2001). In 2004,
81% of polled New Yorkers voiced concern over the state’s late budget, and 46% said
that it made them more willing to vote out incumbents (Caruso 2004).
3. When New Mexico’s budget was delayed in 2000, Governor Gary Johnson
and the legislative leaders all polled poorly and “New Mexico voters faulted Johnson
and lawmakers almost equally for their failure to reach agreement during the session
on a $3 billion budget” (“Voters Unimpressed with Johnson, Lawmakers,” Albuquerque
Journal, 19 March 2000, A1).
4. Unified government does not guarantee executive-legislative agreement over
the budget. In Massachusetts, for instance, Democratic governor Michael Dukakis
consistently had his budget rewritten by the legislature, which was overwhelmingly
controlled by his own party (Beyle 2004; Rosenthal 1990).
5. Our application of this model operates as a metaphor for the informal negotiations between the governor and legislative leaders—such as California’s “Big Five”
or Illinois’s “Four Tops”—that take place behind closed doors and allow either side to
initiate negotiations or to make detailed counteroffers. We believe that ignoring these
negotiations and focusing solely on budget bills and vetoes neglects the key dynamics
that drive interbranch bargaining.
Who Blinks First
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6. By contrast, Alt and Lowry (1994, 813) specify the reversion point of their
model as follows: “If the legislature fails to make a proposal or if a proposal is vetoed
and the veto is sustained, then we assume that the budget is set equal to its reversion
level, which is the previous year’s expected levels plus any persistent effects of the
unforeseen shock.” Indeed, as we note, nine states explicitly permit continuing resolutions that would preserve the status quo in this way. But, we argue, even in these states,
preserving the status quo is no more attractive to the legislature than it is to the governor.
Whatever happens when a budget is late, whether it is a shutdown or a continuing
resolution, imposes high costs on both branches of government. The payoff that both
branches receive from the ultimate deal is eroded, preventing legislators from credibly
threatening to live with the status quo if the governor rejects their offer.
7. Since the Nash prediction is quite vague in this case—any division of the
dollar can be reached in the first round in equilibrium, because players can make threats
that are not credible—Osborne and Rubinstein (1990) employed Selten’s (1975) notion
of a subgame perfect equilibrium, which requires that best responses be played at
every point in the game that begins a subgame (see Morrow 1994). Subgame perfection generally refines the set of acceptable equilibrium strategies and, in this case,
generates a unique prediction.
8. Governors can exert influence over the size of government even in setter
models, as Alt and Lowry describe: “Divided cases produce target levels somewhere
between those of the parties” (1994, 820) and “In neither case does the legislature get
all it wants . . . as it must consider the threat of a veto” (2000, 1042). Nevertheless, the
power that governors have in a setter model is primarily negative; the veto gives them
the ability to put a break on high-spending legislatures. In our staring-match model,
the governor’s ability to make an offer to the legislature regarding the size of government gives the governor positive power to shape spending levels.
9. Legislatures in 30 states have the authority to call their own special sessions
(Council of State Governments 2000), but they are often forced to do so by a governor’s
veto. Although special sessions are not often called to resolve legislative-executive
conflicts, the threat of a special session is not unimportant. Delayed bargains are off
the equilibrium path of Rubinstein’s basic model, but they are weapons that do not
need to be unsheathed to be powerful.
10. Even the lowest-paid governor, Maine’s chief executive, earns $70,000 a
year (Council of State Governments 2005).
11. Interview by Thad Kousser, Salem, Oregon, July 8, 2001.
12. More than a month after New Mexico’s one-month session came to a close
without a budget deal, the governor called the state’s citizen legislators back to Santa
Fe for a special session. One legislator opined that such meetings “[C]ertainly are not
special. They are absolutely routine and, in my opinion, very annoying” (“Only Thing
Special about These Sessions Are Lessons,” Albuquerque Tribune, 4 April 2000, C-2).
In addition to exacting private costs, the session cost the legislature $45,000 a day to
run and generated political controversy. One legislator said, “I think it would behoove
all of us to be out of here by Saturday. I can just see a lot of really ugly newspaper
stories . . . if we’re still in session on April Fool’s” (Mark Hummels, “They’re Back,”
Santa Fe New Mexican, 28 March 2000, A-1). Perhaps because of their hurry, the
legislators passed a budget that was a “political home run” for the governor (“Vetoes
Enact Tax Reduction,” Albuquerque Journal, 22 April 2000, E-3).
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Thad Kousser and Justin H. Phillips
13. In 2007, five of the six states in which a budget standoff dragged on past the
beginning of the next fiscal year—California, Illinois, Michigan, Pennsylvania, and
Wisconsin—had professional legislatures that typically met at least 20 months in a
two-year biennium (personal communication between the authors and Arturo Perez of
the National Conference of State Legislatures). Historically, New York and California,
both of which have highly professionalized legislatures, have been plagued by late
budgets. As of fiscal year 2005, 20 of the last 21 budgets in New York were adopted
well after the legal deadline (McMahon 2005). Similarly, the governor and legislature
in California have failed to adopt a budget on time since fiscal year 1987 (California
Department of Finance 2007).
14. We tested a parallel set of models that used changes in real per capita spending,
rather than changes as a percent of past spending, to measure both the governor’s
proposals and final outcomes. The models yielded substantively identical results to
those presented here.
15. In a separate analysis, we interacted the governor’s proposal with the presence
of divided government, measured first by whether or not the party opposing the governor
controlled both houses of the legislature and second by whether or not the opposing
party controlled one legislative house. Neither interaction was statistically significant.
See analysis by Bowling and Ferguson (2001) and Ferguson (2003) for explorations of
the contingent effects of divided government on state legislative gridlock and the fate
of gubernatorial proposals.
16. Because we used fixed effects, we identified the effect of professionalization
in Models 2 and 3 by the variation in the size of the governor’s proposal.
17. Model 3b uses the dynamic professionalism scores reported by Squire (2007,
220–21). For observations up to fiscal year 1996, we used Squire’s 1986 scores. For
fiscal years 1996 through 2003, we used Squire’s 1996 scores. For the remaining years,
we used Squire’s 2003 scores. We also included professionalism by itself in this model
since, as a dynamic measure, it was no longer correlated with each state’s fixed effects.
Because these results were nearly identical to the ones obtained using the scores reported
by Squire (1992) and used most often in the literature, elsewhere in our analysis we
used only the Squire (1992) scores.
18. The two-way correlations in our dataset are 0.51 between salary and session
length, 0.67 between salary and staff size, and 0.49 between session length and staff size.
19. These categories represent the possible processes identified in the online
version of Grooters and Eckl’s (1998) table, available at the NCSL website <http://
www.ncsl.org/programs/fiscal/lbptabls/lbpc6t4.htm>. We found no consistent relationship between legislative professionalization and these variables.
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